code: def _get_data(format="numpy", n_classes=2):
      X, y = make_classification(
          n_samples=1000,
          n_features=10,
          n_informative=5,
          n_classes=n_classes,
          random_state=seed,
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=seed
      )
      data = ns(
          X_train=X_train, X_test=X_test, y_train=y_train, y_test=y_test
      )
      if format == "h2o":
          for k, v in data.__dict__.items():
              setattr(data, k, h2o.H2OFrame(v))
      return data
  
file_path: h2o-3/h2o-py/tests/testdir_sklearn/pyunit_sklearn_classification_search_pipeline.py
imports: import importlib, inspect, os, sys
  import numpy
  from sklearn.datasets import make_classification
  from sklearn.decomposition import TruncatedSVD
  from sklearn.metrics import accuracy_score, make_scorer
  from sklearn.model_selection import train_test_split, RandomizedSearchCV
  from sklearn.pipeline import Pipeline
  import h2o
  from h2o.cross_validation import H2OKFold
  from h2o.sklearn import h2o_connection, H2OGradientBoostingEstimator, H2OGradientBoostingClassifier, H2OSVD
  from h2o.sklearn.wrapper import H2OConnectionMonitorMixin
  from tests import pyunit_utils, Namespace
label_desc: Use image node_id 1 for calling a global function with example usage: _get_data(format, n_classes) and returns: data